Syed Masroor Anwar
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Syed Masroor Anwar has a total of 7 co-authors. Here are the top co-authors by number of shared publications:
- Ammara Nawaz Cheema
Department of Mathematics, Air University, Islamabad, Pakistan
1 shared publication - Chang-Yun Lin
Department of Applied Mathematics and Institute of Statistics, National Chung Hsing University, Taichung, 40227, Taiwan.
1 shared publication - Fatimah Alshahrani
Department of Mathematical Sciences, College of Science, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
1 shared publication - Ibrahim Mufrah Almanjahie
Department of Mathematics, College of Science, King Khalid University, Abha, Saudi Arabia
1 shared publication - Majid Khan
Department of Mathematics and Statistics Riphah International University Islamabad Pakistan
1 shared publication Naif Almakayeel
Department of Industrial Engineering, College of Engineering, King Khalid University, 61421, Abha, Saudi Arabia
1 shared publication
Latest Publications
- Open Access
- 27 April 2026
- Published by Springer Nature in Scientific Reports
- Vol. 16
Efficient monitoring of exponential processes using memory control charts for healthcare applications
The online version contains supplementary material available at 10.1038/s41598-026-47544-0.
- 27 April 2026
- Published by Springer Nature in Scientific Reports
- Vol. 16
Open AccessEfficient monitoring of exponential processes using memory control charts for healthcare applications
The online version contains supplementary material available at 10.1038/s41598-026-47544-0.
- Open Access
- 23 February 2023
- Published by MDPI AG in Mathematics
- Vol. 11 (5) , 1126
On Designing of Bayesian Shewhart-Type Control Charts for Maxwell Distributed Processes with Application of Boring Machine
The quality characteristic(s) are assumed to follow the normal distribution in many control chart constructions, although this assumption may not hold in some instances. This study proposes the Bayesian-I and Bayesian-II Shewhart-type control charts for monitoring the Maxwell scale parameter in the phase II study. The posterior and predictive distributions are used to construct the control limits for the proposed Bayesian-I and Bayesian-II Shewhart-type control charts, respectively. Various performance indicators, including average run length, quadratic loss, relative average run length, and performance comparison index, are utilized to evaluate the performance of the proposed control charts. The Bayesian-I and Bayesian-II Shewhart-type control charts are compared to their competitive CUSUMV, EWMAV and V control charts. Sensitivity analysis is also performed to study the effect of hyperparameter values on the performance behavior of the proposed control charts. Finally, real-life data is analyzed for the implementation of the proposed control charts.
- 23 February 2023
- Published by MDPI AG in Mathematics
- Vol. 11 (5) , 1126
Open AccessOn Designing of Bayesian Shewhart-Type Control Charts for Maxwell Distributed Processes with Application of Boring Machine
The quality characteristic(s) are assumed to follow the normal distribution in many control chart constructions, although this assumption may not hold in some instances. This study proposes the Bayesian-I and Bayesian-II Shewhart-type control charts for monitoring the Maxwell scale parameter in the phase II study. The posterior and predictive distributions are used to construct the control limits for the proposed Bayesian-I and Bayesian-II Shewhart-type control charts, respectively. Various performance indicators, including average run length, quadratic loss, relative average run length, and performance comparison index, are utilized to evaluate the performance of the proposed control charts. The Bayesian-I and Bayesian-II Shewhart-type control charts are compared to their competitive CUSUMV, EWMAV and V control charts. Sensitivity analysis is also performed to study the effect of hyperparameter values on the performance behavior of the proposed control charts. Finally, real-life data is analyzed for the implementation of the proposed control charts.